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Record W2770959905 · doi:10.3139/217.3553

Fluid Elasticity in Plastic Pipe Extrusion: Loads on Die Barrel

2017· article· en· W2770959905 on OpenAlexaff
Chaimongkol Saengow, A. Jeffrey Giacomin

Bibliographic record

VenueInternational Polymer Processing · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsExtrusionDie (integrated circuit)Barrel (horology)Materials sciencePlug flowAlgebraic expressionMechanicsMechanical engineeringStructural engineeringComposite materialEngineeringAlgebraic numberMathematicsPhysicsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract In large thick plastic pipe extrusion, the residence time in the cooling chamber is long, and the melt inside the pipe sags under its own weight, causing the product to thicken on the bottom (and to thin on the top). To compensate for sag, engineers normally shift the die centerpiece downward. This paper focuses on how this decentering triggers unintended consequences for elastic polymer melts. We employ eccentric cylindrical coordinates, to capture exactly the geometry of our problem, the flow between eccentric cylinders. Specifically, we arrive at an exact analytical expression for the axial and lateral forces on the die barrel using the polymer process partitioning approach, designed for elastic liquids. We choose the Oldroyd 8-constant framework due to its rich diversity of constitutive special cases. Since our main results are in a form of simple algebraic expression along with two sets of curves, they can thus be used not only by engineers, but any practitioner. We close our paper with detailed dimensional worked examples to help practitioners with their pipe die designs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.271
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2017
Admission routes1
Has abstractyes

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